{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "behavioral-jacket",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontManager\n",
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "blond-pulse",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STIXSizeOneSym\n",
      "STIXGeneral\n",
      "DejaVu Sans\n",
      "DejaVu Serif\n",
      "STIXNonUnicode\n",
      "STIXNonUnicode\n",
      "DejaVu Sans Mono\n",
      "STIXSizeThreeSym\n",
      "DejaVu Sans\n",
      "STIXGeneral\n",
      "DejaVu Serif Display\n",
      "cmmi10\n",
      "DejaVu Serif\n",
      "STIXNonUnicode\n",
      "STIXSizeThreeSym\n",
      "DejaVu Sans Mono\n",
      "DejaVu Sans Mono\n",
      "STIXNonUnicode\n",
      "DejaVu Sans Display\n",
      "STIXSizeOneSym\n",
      "cmb10\n",
      "cmr10\n",
      "DejaVu Sans\n",
      "STIXGeneral\n",
      "cmsy10\n",
      "STIXSizeFiveSym\n",
      "cmss10\n",
      "DejaVu Serif\n",
      "cmex10\n",
      "STIXSizeFourSym\n",
      "DejaVu Sans Mono\n",
      "STIXSizeTwoSym\n",
      "STIXSizeFourSym\n",
      "cmtt10\n",
      "DejaVu Serif\n",
      "STIXSizeTwoSym\n",
      "DejaVu Sans\n",
      "STIXGeneral\n",
      "Arial\n",
      "Segoe UI\n",
      "Microsoft YaHei\n",
      "Palatino Linotype\n",
      "Segoe UI\n",
      "Arial\n",
      "Comic Sans MS\n",
      "Microsoft New Tai Lue\n",
      "Calibri\n",
      "Nirmala UI\n",
      "Cambria\n",
      "Times New Roman\n",
      "Ebrima\n",
      "Leelawadee UI\n",
      "Segoe Script\n",
      "KaiTi\n",
      "Consolas\n",
      "Leelawadee UI\n",
      "Microsoft Tai Le\n",
      "Candara\n",
      "Sitka Small\n",
      "Segoe UI\n",
      "Leelawadee UI\n",
      "Arial\n",
      "Corbel\n",
      "Cambria\n",
      "Ink Free\n",
      "Courier New\n",
      "DengXian\n",
      "Corbel\n",
      "Candara\n",
      "Segoe UI Emoji\n",
      "Segoe Print\n",
      "Malgun Gothic\n",
      "HoloLens MDL2 Assets\n",
      "Microsoft PhagsPa\n",
      "Segoe UI Emoji\n",
      "Sitka Small\n",
      "Constantia\n",
      "Georgia\n",
      "Malgun Gothic\n",
      "Sitka Small\n",
      "Tahoma\n",
      "Comic Sans MS\n",
      "Palatino Linotype\n",
      "Comic Sans MS\n",
      "Javanese Text\n",
      "Consolas\n",
      "Segoe Script\n",
      "Segoe Script\n",
      "Constantia\n",
      "Segoe Script\n",
      "Segoe Print\n",
      "Segoe UI\n",
      "Microsoft YaHei\n",
      "Nirmala UI\n",
      "Bahnschrift\n",
      "Corbel\n",
      "Segoe UI\n",
      "Trebuchet MS\n",
      "Myanmar Text\n",
      "Courier New\n",
      "Arial\n",
      "MS Gothic\n",
      "Consolas\n",
      "Gabriola\n",
      "Corbel\n",
      "Malgun Gothic\n",
      "FangSong\n",
      "Segoe UI\n",
      "Calibri\n",
      "Microsoft Tai Le\n",
      "Cambria\n",
      "Trebuchet MS\n",
      "Constantia\n",
      "Consolas\n",
      "SimHei\n",
      "Segoe UI\n",
      "Times New Roman\n",
      "Malgun Gothic\n",
      "Consolas\n",
      "Arial\n",
      "Tahoma\n",
      "Calibri\n",
      "Times New Roman\n",
      "Corbel\n",
      "Ebrima\n",
      "Segoe UI\n",
      "Yu Gothic\n",
      "Calibri\n",
      "Microsoft Sans Serif\n",
      "Palatino Linotype\n",
      "Candara\n",
      "Corbel\n",
      "Georgia\n",
      "Segoe MDL2 Assets\n",
      "DengXian\n",
      "Constantia\n",
      "Courier New\n",
      "Calibri\n",
      "Arial\n",
      "Impact\n",
      "Calibri\n",
      "Corbel\n",
      "Segoe UI\n",
      "Constantia\n",
      "Sylfaen\n",
      "Comic Sans MS\n",
      "Segoe UI Symbol\n",
      "Lucida Sans Unicode\n",
      "Arial\n",
      "Malgun Gothic\n",
      "Microsoft New Tai Lue\n",
      "SimSun-ExtB\n",
      "Symbol\n",
      "Gadugi\n",
      "Trebuchet MS\n",
      "Candara\n",
      "Segoe UI\n",
      "Constantia\n",
      "Microsoft New Tai Lue\n",
      "Candara\n",
      "Candara\n",
      "Palatino Linotype\n",
      "Corbel\n",
      "Sitka Small\n",
      "Yu Gothic\n",
      "Trebuchet MS\n",
      "DengXian\n",
      "Microsoft YaHei\n",
      "Gadugi\n",
      "Lucida Console\n",
      "Marlett\n",
      "Yu Gothic\n",
      "Segoe UI\n",
      "SimHei\n",
      "Verdana\n",
      "Segoe UI\n",
      "Times New Roman\n",
      "Nirmala UI\n",
      "Franklin Gothic Medium\n",
      "Segoe Print\n",
      "Microsoft Sans Serif\n",
      "Tahoma\n",
      "Corbel\n",
      "Cambria\n",
      "Constantia\n",
      "Franklin Gothic Medium\n",
      "Ebrima\n",
      "Cambria\n",
      "Comic Sans MS\n",
      "Javanese Text\n",
      "Palatino Linotype\n",
      "SimSun\n",
      "Sitka Small\n",
      "Calibri\n",
      "Segoe UI Symbol\n",
      "Candara\n",
      "Courier New\n",
      "Leelawadee UI\n",
      "Verdana\n",
      "Arial\n",
      "Franklin Gothic Medium\n",
      "Ink Free\n",
      "Times New Roman\n",
      "KaiTi\n",
      "Calibri\n",
      "Malgun Gothic\n",
      "Candara\n",
      "Corbel\n",
      "Gadugi\n",
      "MV Boli\n",
      "SimSun\n",
      "Nirmala UI\n",
      "Microsoft YaHei\n",
      "Verdana\n",
      "Segoe UI\n",
      "Consolas\n",
      "Microsoft Himalaya\n",
      "Myanmar Text\n",
      "Segoe UI Historic\n",
      "Verdana\n",
      "Sitka Small\n",
      "Microsoft JhengHei\n",
      "DengXian\n",
      "Verdana\n",
      "Yu Gothic\n",
      "Gabriola\n",
      "Microsoft JhengHei\n",
      "Webdings\n",
      "Candara\n",
      "Franklin Gothic Medium\n",
      "Microsoft YaHei\n",
      "Microsoft PhagsPa\n",
      "Consolas\n",
      "Microsoft JhengHei\n",
      "Calibri\n",
      "Segoe UI\n",
      "Comic Sans MS\n",
      "Segoe UI\n",
      "Georgia\n",
      "Leelawadee UI\n",
      "DengXian\n",
      "Microsoft JhengHei\n",
      "Calibri\n",
      "Georgia\n",
      "Trebuchet MS\n",
      "Georgia\n",
      "Calibri\n",
      "Times New Roman\n",
      "Courier New\n",
      "Leelawadee UI\n",
      "Calibri\n",
      "DengXian\n",
      "Ebrima\n",
      "Microsoft Tai Le\n",
      "Yu Gothic\n",
      "Courier New\n",
      "Segoe UI\n",
      "Verdana\n",
      "Comic Sans MS\n",
      "Webdings\n",
      "Microsoft Himalaya\n",
      "Sylfaen\n",
      "Sitka Small\n",
      "Microsoft New Tai Lue\n",
      "Palatino Linotype\n",
      "Microsoft PhagsPa\n",
      "Corbel\n",
      "Nirmala UI\n",
      "Nirmala UI\n",
      "MS Gothic\n",
      "Microsoft YaHei\n",
      "Trebuchet MS\n",
      "Segoe UI\n",
      "Segoe UI\n",
      "Wingdings\n",
      "Lucida Sans Unicode\n",
      "Verdana\n",
      "Candara\n",
      "Segoe UI\n",
      "Gadugi\n",
      "Georgia\n",
      "Microsoft JhengHei\n",
      "Georgia\n",
      "Microsoft Yi Baiti\n",
      "Palatino Linotype\n",
      "Times New Roman\n",
      "Segoe UI\n",
      "FangSong\n",
      "Wingdings\n",
      "Cambria\n",
      "Cambria\n",
      "Segoe UI\n",
      "Candara\n",
      "Arial\n",
      "Comic Sans MS\n",
      "Consolas\n",
      "Cambria\n",
      "Microsoft JhengHei\n",
      "SimSun-ExtB\n",
      "Yu Gothic\n",
      "Yu Gothic\n",
      "Yu Gothic\n",
      "Mongolian Baiti\n",
      "Impact\n",
      "MingLiU-ExtB\n",
      "Segoe Print\n",
      "MingLiU-ExtB\n",
      "Microsoft Yi Baiti\n",
      "Courier New\n",
      "Tahoma\n",
      "Mongolian Baiti\n",
      "Palatino Linotype\n",
      "Corbel\n",
      "Bahnschrift\n",
      "Segoe MDL2 Assets\n",
      "Segoe UI\n",
      "Trebuchet MS\n",
      "Segoe UI\n",
      "Trebuchet MS\n",
      "Segoe UI\n",
      "Courier New\n",
      "HoloLens MDL2 Assets\n",
      "Microsoft PhagsPa\n",
      "Symbol\n",
      "Candara\n",
      "Verdana\n",
      "Segoe UI Historic\n",
      "Times New Roman\n",
      "Sitka Small\n",
      "Lucida Console\n",
      "Myanmar Text\n",
      "Myanmar Text\n",
      "Georgia\n",
      "Constantia\n",
      "Microsoft Tai Le\n",
      "Arial\n",
      "MV Boli\n"
     ]
    }
   ],
   "source": [
    "fm = FontManager()\n",
    "for font in fm.ttflist:\n",
    "    print(font.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "systematic-mentor",
   "metadata": {},
   "source": [
    "## 男生相关操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "indie-domain",
   "metadata": {},
   "source": [
    "### 分箱操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "documentary-southeast",
   "metadata": {},
   "outputs": [],
   "source": [
    "boy = pd.read_excel('./体测分数_男生.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "excited-norwegian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0   1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1   1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2   1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3   1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4   1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "\n",
       "   男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0      0  2785      62  170  72.599998  25.120001  \n",
       "1     60  3133      68  174  52.700001  17.410000  \n",
       "2      0  3901      80  169  46.500000  16.280001  \n",
       "3      0  4946     100  183  79.699997  23.799999  \n",
       "4     68  3538      74  171  54.700001  18.709999  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "laden-house",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.83, 4.65]     245\n",
       "(3.008, 3.83]    195\n",
       "(4.65, 5.47]      16\n",
       "Name: 男1000米跑, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#男生1000米跑分箱操作\n",
    "boy1 = boy['男1000米跑']\n",
    "cond = boy1.values !=0  #选取成绩不为0的数据\n",
    "boy1 = boy1[cond]\n",
    "boy_1000 = pd.cut(x = boy1,bins = 3)\n",
    "boy_count1 = boy_1000.value_counts()\n",
    "boy_count1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "japanese-china",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.978, 8.333]     223\n",
       "(8.333, 15.667]    199\n",
       "(15.667, 23.0]      20\n",
       "Name: 男引体, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#男生引体分箱操作\n",
    "boy_up = boy['男引体']\n",
    "cond = boy_up.values != 0 #选取成绩不为0的数据\n",
    "boy_up = boy_up[cond]\n",
    "boy_up\n",
    "boy_count2 = pd.cut(x = boy_up,bins = 3).value_counts()\n",
    "boy_count2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "looking-holiday",
   "metadata": {},
   "source": [
    "### 饼图绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "rough-clinton",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1451d435f48>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#男生1000米跑\n",
    "plt.figure(figsize = (8,5.5))\n",
    "\n",
    "plt.pie(x = boy_count1.values,\n",
    "        wedgeprops={'width' : 0.4},\n",
    "        autopct='%0.2f%%',\n",
    "        pctdistance=0.8,\n",
    "        textprops={'fontsize':12},\n",
    "        labels = boy_count1.index)\n",
    "\n",
    " \n",
    "plt.title(label = '    男1000米跑成绩分级    ',\n",
    "         fontfamily = 'KaiTi',\n",
    "         fontsize = 18,\n",
    "         backgroundcolor = '#4682B4',\n",
    "         color = 'w')\n",
    "\n",
    "plt.legend(bbox_to_anchor = [0,0.2,1.3,0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "traditional-spanish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x145168f7e08>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#男生引体\n",
    "plt.figure(figsize = (8,5.5))\n",
    "plt.pie(x = boy_count2,\n",
    "       labels = boy_count2.index,\n",
    "       autopct='%0.2f%%',\n",
    "       pctdistance=0.8,\n",
    "       wedgeprops={'width':0.4},\n",
    "       textprops={'fontsize':12})\n",
    "plt.title(label = '    男生引体成绩分级    ',\n",
    "         fontfamily = 'KaiTi',\n",
    "         fontsize = 18,\n",
    "         backgroundcolor = '#4682B4',\n",
    "         color = 'w')\n",
    "plt.legend(bbox_to_anchor = [0,0.2,1.3,0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jewish-bruce",
   "metadata": {},
   "source": [
    "## 女生相关操作"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-california",
   "metadata": {},
   "source": [
    "### 女800米跑统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "different-setting",
   "metadata": {},
   "outputs": [],
   "source": [
    "girl = pd.read_excel('./体测分数_女生.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "painted-disabled",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0     85  3775     100  163.0  51.299999  19.309999  \n",
       "1     66  3683     100  163.0  66.599998  25.070000  \n",
       "2     76  3331     100  157.0  60.000000  24.340000  \n",
       "3     85  3701     100  160.0  50.700001  19.799999  \n",
       "4     70  3592     100  167.0  63.900002  22.910000  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "published-catalyst",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.24, 4.36]     506\n",
       "(4.36, 5.48]      36\n",
       "(2.12, 3.24]      19\n",
       "(0.996, 2.12]      1\n",
       "Name: 女800米跑, dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分箱操作\n",
    "girl_800 = girl['女800米跑']\n",
    "cond = (girl_800.values != 0) & (girl_800 < 100) #去掉异常值\n",
    "girl_800 = girl_800[cond]\n",
    "girl_count1 = pd.cut(girl_800,bins = 4 ).value_counts()\n",
    "girl_count1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "thousand-philosophy",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.family'] = 'KaiTi'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "pretty-shell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#直方图绘制\n",
    "plt.figure(figsize=(8,5.5))\n",
    "plt.bar(x = [ '(3.24, 4.36]','(4.36, 5.48]','(2.12, 3.24]','(0.996, 2.12]'] ,\n",
    "        height = girl_count1,\n",
    "        width=0.5,\n",
    "        color = plt.cm.RdBu_r(np.linspace(0,1,4)))\n",
    "plt.title(label = '     女生800米跑成绩分布      ',\n",
    "          fontsize=24,\n",
    "          color='w',\n",
    "          pad = 30 ,\n",
    "          backgroundcolor='#4682B4')\n",
    "plt.grid(ls = '--',axis = 'y')\n",
    "plt.ylabel(ylabel='人数',fontsize = 20,labelpad=10,)\n",
    "plt.xlabel(xlabel='成绩',fontsize = 20 ,labelpad=10)\n",
    "plt.box(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outdoor-regard",
   "metadata": {},
   "source": [
    "### 女跳远统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "aquatic-diagram",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0     85  3775     100  163.0  51.299999  19.309999  \n",
       "1     66  3683     100  163.0  66.599998  25.070000  \n",
       "2     76  3331     100  157.0  60.000000  24.340000  \n",
       "3     85  3701     100  160.0  50.700001  19.799999  \n",
       "4     70  3592     100  167.0  63.900002  22.910000  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "opening-reliance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(136.0, 164.0]      313\n",
       "(164.0, 192.0]      247\n",
       "(192.0, 220.0]       13\n",
       "(107.888, 136.0]      5\n",
       "Name: 女跳远, dtype: int64"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#分箱操作\n",
    "girl_dump = girl['女跳远']\n",
    "cond = girl_dump!= 0  #去掉成绩为0的异常数据\n",
    "girl_dump = girl_dump[cond]\n",
    "girl_count2 = pd.cut(girl_dump,bins = 4).value_counts()\n",
    "girl_count2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "younger-vermont",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#直方图绘制\n",
    "plt.figure(figsize = (8,5.5))\n",
    "plt.barh(y = ['(136.0, 164.0] ','(164.0, 192.0]','(192.0, 220.0] ','(107.888, 136.0] '][::-1],\n",
    "         width = girl_count2.values[::-1],\n",
    "         color = plt.cm.RdBu(np.linspace(0,1,4)))\n",
    "plt.title(label='    女生跳远成绩分布    ',\n",
    "         backgroundcolor = '#4682B4',\n",
    "         color = 'w',\n",
    "         fontsize = 24,\n",
    "         pad = 30)\n",
    "plt.ylabel(ylabel='成绩',fontsize = 20 , labelpad = 10)\n",
    "plt.xlabel(xlabel = '人数',fontsize = 20 ,labelpad= 10)\n",
    "plt.grid(ls = '--',axis = 'x')\n",
    "plt.box(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ignored-procurement",
   "metadata": {},
   "source": [
    "## 男女生体重指数统计"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secure-silence",
   "metadata": {},
   "source": [
    "### 男生BMI分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "modern-lesson",
   "metadata": {},
   "outputs": [],
   "source": [
    "boy_bmi = round(boy['BMI'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "artistic-coverage",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.3"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boy_bmi.values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "corresponding-supply",
   "metadata": {},
   "outputs": [],
   "source": [
    "boy_bmi = pd.cut(x= boy_bmi,\n",
    "                 bins = [0,16.4,23.2,26.3,39.3],\n",
    "                 labels=['低体重','正常','超重','肥胖'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "exposed-heritage",
   "metadata": {},
   "outputs": [],
   "source": [
    "boy_count3 = boy_bmi.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modified-concert",
   "metadata": {},
   "source": [
    "### 女生BMI分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "innocent-finland",
   "metadata": {},
   "outputs": [],
   "source": [
    "girl_bmi = round(girl['BMI'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "scientific-prophet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.1"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "girl_bmi.values.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "bulgarian-factor",
   "metadata": {},
   "outputs": [],
   "source": [
    "girl_bmi = pd.cut(x = girl_bmi,\n",
    "                  bins = [0,16.4,22.7,25.2,36.1],\n",
    "                  labels=['低体重','正常','超重','肥胖'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "relevant-effectiveness",
   "metadata": {},
   "outputs": [],
   "source": [
    "girl_count3 = girl_count3.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "documented-payment",
   "metadata": {},
   "source": [
    "### 绘制嵌套饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "biblical-handbook",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '     高一年级男女生体重指数比例     ')"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.pie(x = boy_count3 ,\n",
    "       radius = 1,\n",
    "       wedgeprops = {'width': 0.4,\n",
    "                    'linewidth':5,\n",
    "                    'edgecolor':'w'},\n",
    "       autopct='%0.2f%%',\n",
    "       pctdistance = 0.8,\n",
    "       labels = boy_count3.index,\n",
    "       labeldistance=1.1,\n",
    "       textprops={'fontsize':15},\n",
    "       colors = plt.cm.Blues_r(np.linspace(0,1,4)))\n",
    "plt.pie(x = girl_count3,\n",
    "        radius= 0.6,\n",
    "        wedgeprops = {'width':0.6,\n",
    "                     'linewidth':5 ,\n",
    "                     'edgecolor':'w'},\n",
    "       autopct = '%0.2f%%',\n",
    "       pctdistance=0.75,        \n",
    "       labels = girl_count3.index ,\n",
    "       labeldistance = 0.25,\n",
    "       textprops={'fontsize' : 14},\n",
    "       colors = plt.cm.Reds_r(np.linspace(0,1,4)))\n",
    "plt.legend(bbox_to_anchor = [0,0.2,1.3,0.2])\n",
    "plt.title(label = '     高一年级男女生体重指数比例     ',\n",
    "         backgroundcolor = '#4682B4',\n",
    "         color = 'w',\n",
    "         fontsize = 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "through-learning",
   "metadata": {},
   "outputs": [],
   "source": [
    "#想把男生、女生图例分开做，但是没有研究出来-_-"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
